A few weeks ago I read Kevin Indig's analysis of three million ChatGPT responses and around 18,000 verified citations, and I had the same mildly frustrating reaction every working SEO has had to that piece. The findings are concrete enough to act on. The tools to act on them are not.
Indig's research is direct. Cited content uses definite phrasing nearly twice as often as uncited content. Cited passages average around 20.6% entity density compared to 5-8% for ordinary prose. About 44.2% of citations come from the first 30% of the page. None of those numbers are abstractions; they are dimensions you can measure on any article you publish.
The Hedge Language Auditor at /tools/hedge-language-auditor/ measures all four. It runs entirely in the browser, takes a URL or pasted text, and produces a composite 0-100 score plus per-framework sub-scores, plus a list of every hedge word found, plus the section-leading sentences that fail BLUF, plus a placement map of the dominant noun phrase across the page.
The four checks the tool runs
1. BLUF (bottom-line-up-front): takes each H2 or H3 section, extracts the first sentence, and tests whether that sentence carries an assertion verb without a leading hedge. Rate of compliance is the score. Target: 70%+.
2. Hedge density: counts hedge words (may, might, could potentially, seems, appears, likely, often, sometimes, arguably, ...) per total sentences. Target: under 1.5%.
3. Entity density: approximates Indig's metric by counting capitalized non-sentence-start tokens, named numbers, and currency/percent values, divided by total words. Target band: 12% to 20%.
4. Strategic repetition: extracts the most-frequent noun phrase and counts its placements across the page. Target: at least 3 placements (introduction, mid-article reminder, conclusion), per the published research on transformer retrieval-window matching.
Each framework contributes 25 points to the composite score. Sub-scores reflect how close each dimension is to the published target band, not a binary pass/fail.
What the tool catches that I miss while writing
Three patterns showed up consistently in my own back-test of around 30 of my published posts.
The most common miss was section-leading sentences that read as transitions instead of assertions. "Let's look at how this works" or "There are several reasons for this" or "Before we get into the specifics" all fail BLUF. The fix is small: rewrite each first-sentence-of-section as the answer the section will produce. The tool surfaces every offender on a single page.
The second most common miss was hedge accumulation in conclusion paragraphs. Hedges aggregate at the end of pieces because writers (myself included) want to soften the close. The tool exposes that pattern in the hedge-word list.
The third miss was repetition gap. The strongest claim of a 1,500-word piece often appears in only one location, usually the introduction. Indig's research and Petrovic's earlier work on retrieval-window matching both point at the same fix: 2 to 3 placements of the same idea, rephrased for context. The tool's placement map shows you which percentage through the page each instance lives at.
How it pairs with the existing AI Citation Readiness tool
This site already had a 14-signal AI Citation Readiness audit that scores word count, fact density, schema, canonical, author, dates, lists, and similar structural inputs. The Hedge Language Auditor sits adjacent to that. The 14-signal tool answers "is the page well-formed for citation"; the Hedge Language Auditor answers "is the writing itself the kind that gets cited."
Both run in the browser without sending your content anywhere. Both output structural recommendations, not vague suggestions.
I also added the four framework dimensions to AI Citation Readiness as additive checks 15-18 (BLUF compliance, hedge density, entity density, strategic repetition). If you only want to run one tool, run AI Citation Readiness for the full 18-check audit. Use the standalone Hedge Language Auditor when you want the deeper diagnostics: every hedge word listed, every BLUF-fail section quoted, the placement map for strategic repetition.
Why this is not "AI gaming"
A predictable concern about citation-optimization tools is that they encourage gaming the system. The honest counter, which I covered in the broader citation frameworks post, is that the four frameworks are simply how good non-fiction has always read. Military communication has used BLUF for decades. Encyclopedia entries use definite phrasing because that is what definitions are. Good textbooks use entities at high density. Good lecturers repeat their key points.
The reason the same patterns work for AI is that AI was trained on the same body of writing. Optimizing for these frameworks is not a trick. It is writing the way the corpus the model was trained on already writes.
Limits, in plain terms
The tool is heuristic. Entity density approximation will undercount in domains where capitalization conventions differ (lowercase brand names, language conventions outside English). The BLUF check uses an assertion-verb regex that catches the common patterns but will miss unusual phrasings. Strategic repetition only finds 2-to-4-word noun phrases; it will not detect repetition expressed as a single rare proper noun.
Citation behavior depends on many factors outside any single page: domain authority, recency, the prompt the user gave, the RAG pipeline configuration, whether your page is on result page one of organic Google in the first place. Ahrefs's research shows 76% of AI Overview citations come from the top 10 organic results. Writing optimization is the second move after ranking, not a substitute for ranking.
Related reading
- Why ChatGPT Cites Your Competitor And Not You for the deep dive on the four frameworks and the underlying research
- AI Citation Readiness audit for the full 18-check version
- Your Site Might Be Invisible To ChatGPT for the infrastructure layer that runs underneath citation
- The Conversation Has Moved Past The Model for the runtime layer that decides whether your page even gets read
Fact-check notes and sources
- Kevin Indig's research on AI citation patterns: The science of how AI pays attention, Growth Memo, The science of what AI actually rewards
- Search Engine Land coverage of the 44% first-third finding: SEL article
- Dan Petrovic's prior work on human-friendly = AI-friendly content: DEJAN Marketing
- Ahrefs on AI citation overlap with ranking: 76% of AI Overview citations from top 10
Heuristic on-page analysis, not a citation guarantee. Patterns described reflect public research current to mid-2026 and may evolve as retrieval pipelines change. Test on your own content; treat single-month results as one data point.
